Scale selection for anisotropic diffusion filter by Markov random field model

The selection of stopping time (i.e., scale) significantly affects the performance of anisotropic diffusion filter for image denoising. This paper designs a Markov random field (MRF) scale selection model, which selects scales for image segments, then the denoised image is the composition of segments at their optimal scales in the scale space. Firstly, statistics-based scale selection criteria are proposed for image segments. Then we design a scale selection energy function in the MRF framework by considering the scale coherence between neighboring segments. A segment-based noise estimation algorithm is also developed to estimate the noise statistics efficiently. Experiments show that the performance of MRF scale selection model is much better than the previous global scale selection schemes. Combined with this scale selection model, the anisotropic diffusion filter is comparable to or even outperform the state-of-the-art denoising methods in performance.

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